SARS-CoV-2 contains certain molecules that are related to the presence of immunothrombosis. Here, we review the pathogen and damage-associated molecular patterns. We also study the imbalance of different molecules participating in immunothrombosis, such as tissue factor, factors of the contact system, histones, and the role of cells, such as endothelial cells, platelets, and neutrophil extracellular traps. Regarding the pathogenetic mechanism, we discuss clinical trials, case-control studies, comparative and translational studies, and observational studies of regulatory or inhibitory molecules, more specifically, extracellular DNA and RNA, histones, sensors for RNA and DNA, as well as heparin and heparinoids. Overall, it appears that a network of cells and molecules identified in this axis is simultaneously but differentially affecting patients at different stages of COVID-19, and this is characterized by endothelial damage, microthrombosis, and inflammation.

Background The genomic sequences of SARS‐CoV‐2 worldwide are publicly available and are derived from studies due to the increase in the number of cases. The importance of study of mutations is related to the possible virulence and diagnosis of SARS‐CoV‐2. Aims To identify circulating mutations present in SARS‐CoV‐2 genomic sequences in Mexico, Belize and Guatemala to find out if the same strain spread to the south, and analyze the specificity of the primers used for diagnosis in these samples. Methods 23 complete SARS‐CoV‐2 genomic sequences, available in the GISAID database from May 8 to September 11, 2020 were analyzed and aligned versus the genomic sequence reported in Wuhan, China (NC_045512.2), using Clustal Omega. Open reading frames were translated using the ExPASy Translate Tool and UCSF Chimera (v.1.12) for amino acid substitutions analysis. Finally, the sequences were aligned versus primers used in the diagnosis of COVID‐19. Results 187 distinct variants were identified, of which 102 are missense, 66 synonymous and 19 non‐coding. P4715L and P5828L substitutions in replicase polyprotein were found, as well as D614G in spike protein and L84S in ORF8 in Mexico, Belize and Guatemala. The primers design by CDC of US showed a positive E‐value. Conclusion The genomic sequences of SARS‐CoV‐2 in Mexico, Belize and Guatemala present similar mutations related to a virulent strain of greater infectivity, which could mean a greater capacity for inclusion in the host genome and be related to an increased spread of the virus in these countries, further, its diagnosis would be affected.

The presence of isoforms of β-glucosidase has been reported in some grasses such as sorghum, rice and maize. This work aims to extract and characterize isoform II in β-glucosidase from S. edule. A crude extract was prepared without buffer solution and adjusted to pH 4.6. Contaminating proteins were precipitated at 4 °C for 24 h. The supernatant was purified by chromatography on carboxymethyl cellulose (CMC) column, molecular exclusion on Sephacryl S-200HR, and exchange anionic on QFF column. Electrophoretic analyzes revealed a purified enzyme with aggregating molecular complex on SDS-PAGE, Native-PAGE, and AU-PAGE. Twelve peptides fragments were identified by nano liquid chromatography-tandem mass spectrometry (nano LC-ESI-MS/MS), which presented as 61% identical to Cucurbita moschata β-glucosidase and 55.74% identical to β-glucosidase from Cucumis sativus, another Cucurbitaceous member. The relative masses which contained 39% hydrophobic amino acids ranged from 982.49 to 2,781.26. The enzyme showed a specificity to β-d-glucose with a K_m of 4.59 mM, a V_max value of 104.3 μM∙min⁻¹ and a k_cat of 10,087 μM∙min⁻¹ using p-nitrophenyl-β-D-glucopyranoside. The presence of molecular aggregates can be attributed to non-polar amino acids. This property is not mediated by a β-glucosidase aggregating factor (BGAF) as in grasses (maize and sorghum). The role of these aggregates is discussed.

Agriculture is established as a fundamental activity that sustains the basis of our food supply. Through techniques that respect and preserve the environment, it is possible to achieve efficient agricultural production that meets the growing needs of our population, thus bringing us closer to the longed-for food sovereignty and meeting the sustainable development goal of zero hunger proposed by the United Nations (UN). In this review, several research studies that explore the implementation of new fertilization techniques are presented. These techniques use growth-promoting bacteria, which operate through both direct and indirect mechanisms. Studies show how these bacteria can significantly improve the production of grains and vegetables essential for food in Mexico and other countries. Thus, agriculture becomes a key pillar for a more sustainable and food-secure future.

Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world validation efforts. Methods: We assembled a synthetic dataset of 28,000 records, with 7000 for each disease—Dengue, Zika, and Chikungunya—plus Influenza as a negative control. These records were obtained from the existing literature. A binary matrix with 67 symptoms was created for detailed statistical analysis using Odds Ratios, Chi-Square, and symptom-specific conditional prevalence to validate the clinical relevance of the simulated data. This dataset was used to train and evaluate various algorithms, including Multi-Layer Perceptron (MLP), Narrow Neural Network (NN), Quadratic Support Vector Machine (QSVM), and Bagged Tree (BT), employing multiple performance metrics: accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and Cohen’s kappa coefficient. Results: The dataset aligns with the PAHO guidelines. Similar findings are observed in other arboviral databases, confirming the validity of the synthetic dataset. A notable performance across all evaluated metrics was observed. The NN model achieved an overall accuracy of 0.92 and an AUC above 0.98, with precision, sensitivity, and specificity values exceeding 0.85, and an average Uniform Cohen’s Kappa of 0.89, highlighting its ability to reliably distinguish between Dengue and Influenza, with a slight decrease between Zika and Chikungunya. Conclusions: These models could accelerate early diagnosis of arboviral diseases by leveraging encoded symptom features for Machine Learning and Deep Learning approaches, serving as a support tool in regions with limited healthcare access without replacing clinical medical expertise.